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Record W1533323647

Non-motorised public transport : the past, the present, the future

2010· article· en· W1533323647 on OpenAlexaboutno aff
Mamun Muntasir Rahman, G D'Este, Jonathan M. Bunker

Bibliographic record

VenueQUT ePrints (Queensland University of Technology) · 2010
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsPublic transportTaxisFlexibility (engineering)SustainabilityHappeningLatin AmericansNoveltyBusinessTransport engineeringMode of transportEngineeringPolitical scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

Non-motorized public transport (NMPT) involves cycle-powered vehicles that carry several passengers and a small amount of goods; and provide flexible hail-and-ride services. Effectively they are non-motorized taxis. NMPT is widespread in developing countries, where it caters for a wide range of mobility needs. Common forms include cycle-rickshaw (Bangladesh, India), becak (Indonesia), cyclos (Vietnam, Cambodia), bicitaxi (Columbia, Cuba). Over the last 10-15 years there has also been a re-emergence of NMPT in the form of pedicabs in many developed countries because of the operating flexibility of NMPT, its eco-sustainability, and its ability to operate where use of motorized vehicles is restricted. In particular, in cities such as Berlin, London, New York and Vancouver, pedicabs are making the transition from ‘novelty’ to ‘serious’ transport mode. This is creating new transport policy/planning questions about pedicab operation and integration. This paper examines the phenomenon of NMPT and where it is heading. It uses case studies from Asia/Latin America and Europe/North America to examine emerging NMPT issues and possible responses, and how this may affect NMPT in Australia and New Zealand where it is still somewhat a ‘novelty’ but has potential as both an opportunity and a challenge.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.177
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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